Graph Embedding for Pattern Analysis -

Graph Embedding for Pattern Analysis

Yun Fu, Yunqian Ma (Herausgeber)

Buch | Softcover
260 Seiten
2014
Springer-Verlag New York Inc.
978-1-4899-9062-4 (ISBN)
129,98 inkl. MwSt
Graph Embedding for Pattern Recognition covers theory methods, computation, and applications widely used in statistics, machine learning, image processing, and computer vision. This book presents the latest advances in graph embedding theories, such as nonlinear manifold graph, linearization method, graph based subspace analysis, L1 graph, hypergraph, undirected graph, and graph in vector spaces. Real-world applications of these theories are spanned broadly in dimensionality reduction, subspace learning, manifold learning, clustering, classification, and feature selection. A selective group of experts contribute to different chapters of this book which provides a comprehensive perspective of this field.

Dr. Yun Fu is a professor at the State University of New York at Buffalo Dr. Yunqian Ma is a senior principal research scientist of Honeywell Labs at the Honeywell International Inc.

Multilevel Analysis of Attributed Graphs for Explicit Graph Embedding in Vector Spaces.- Feature Grouping and Selection over an Undirected Graph.- Median Graph Computation by Means of Graph Embedding into Vector Spaces.- Patch Alignment for Graph Embedding.- Feature Subspace Transformations for Enhancing K-Means Clustering.- Learning with ℓ1-Graph for High Dimensional Data Analysis.- Graph-Embedding Discriminant Analysis on Riemannian Manifolds for Visual Recognition.- A Flexible and Effective Linearization Method for Subspace Learning.- A Multi-Graph Spectral Approach for Mining Multi-Source Anomalies.- Graph Embedding for Speaker Recognition.

Erscheint lt. Verlag 13.12.2014
Zusatzinfo VIII, 260 p.
Verlagsort New York
Sprache englisch
Maße 155 x 235 mm
Themenwelt Informatik Theorie / Studium Künstliche Intelligenz / Robotik
Technik Elektrotechnik / Energietechnik
Technik Nachrichtentechnik
Schlagworte computer vision • dimensionality reduction • discriminant analysis • graph embedding • Hypergraph • machine learning • manifold learning • pattern recognition • subspace learning
ISBN-10 1-4899-9062-3 / 1489990623
ISBN-13 978-1-4899-9062-4 / 9781489990624
Zustand Neuware
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